REGEN-2069 trial of mAb as COVID-19 prophylaxis

Monoclonal antibody (mAb), REGEN-COV, for prevention of COVID-19.

• Primary endpoint was symptomatic infection within 28 days.

• SARS-CoV-2 naïve unvaccinated participants enrolled within 96 hours of household index case.

• Randomized 1:1 to mAb vs. placebo.

• Monitored continuously for symptoms. Also, weekly nasopharyngeal swabs for RT-qPCR and serology for anti-nucleocapsid antibodies at 28 days.

  • PCR indicates ongoing viral shedding.
  • Positive serology is a marker of immune response to infection.

• 81.4% reduction in risk of symptomatic infection (odds ratio, 0.17; 95% CI, 0.09 to 0.33; p < 0.001).

Data assimilation

Strategy: combine PCR, symptom, and serology data.

• No one data stream completely captures infection.
• More kicks at the can to detect infections.
• Scientific collaborators use their clinical and immunological expertise to help us formulate a model.

Modeling multiple data streams (big picture)

Study participants transition through discrete states of infection and immune response:

Idea: PCR, symptoms, and serology are breadcrumbs about each participant’s trajectory.

Model formulation (big picture)

A multistate model is characterized by

• a set of distinct states, and
• transition intensities (like hazards for survival) from one state to another, \[ \begin{align*} \scriptsize \lambda_{ij}(t\mid H(t^-)) = \underset{\Delta t \downarrow 0}{\mathrm{lim}}\frac{\Pr\left(\text{State at }t^-+\Delta t = i\ \vert\ \text{State at }t^- = i,\ \text{history up to }t^-\right)}{\Delta t} \end{align*} \] where time, \(t\), is the time since entry to the current state, \(i\).
• Convenient to adopt a proportional transition intensity parameterization for treatment effects.

Temporal dynamics of clinical progression:

• Markov models:

  • Transition intensities independent of state sojourn.
  • Can be used to analyze data under intermittent observation and hidden states.
  • Often unrealistic, commonly used for computational reasons.

• Semi-Markov models:

  • Transition intensities depend on duration of state occupancy.
  • More realistic but difficult to fit.

Here’s the problem

Data are a coarse reflection of a latent biological process in continuous time.

• Coarse data with different temporal resolutions and complicated censoring.
• Incomplete identification of state labels at observation times.
• Likelihood is a product of transition probabilities over inter-observation intervals.

Methodological contribution

Recall, in the expectation-maximization (EM) algorithm we alternate between:

• E-step: calculate the expected complete data log-likelihood, i.e., Q-function, to average over missing data.

• M-step: maximize the Q-function.

• Wash, rinse, repeat until convergence to obtain an MLE.

Models

Fit 6 models varying in their flexibility.

• Markov model: exponential transition intensities as fit by the msm package in R (only off-the-shelf game in town for panel data with state misclassification).

• Semi-Markov models:

  • Weibull transition intensities (fully parametric).
  • B-splines (flexible) for infection (1-2) + symptom onset (2-4).

Results

We choose the second most flexible model by AIC.

• Gain 100 units of log-likelihood over time-homogeneous Markov model.
• Gain 78.7 units of log-likelihood over parametric Weibull intensity model.

Results

Incidence of symptomatic infection (left) and all-comer infection (right)

• Good fit for incidence of symptomatic infection (continuously observed).
• Large gap in week 1 between observed infections and smooth estimate reflects early infections not caught until first PCR or symptom onset.
• Persistent discrepancy is short shedders not captured by weekly PCR.

Key takeaways from the analysis:

Multifaceted benefit of mAb prophylaxis:

• Reduced the risk of infection (PE = 1 - relative risk):

  • Overall infection: PE = 60.4% (95% CI: 44.9%, 72.5%),
  • Symptomatic: PE = 83.6% (95% CI: 69.4%, 93.1%),
  • Asymptomatic: PE = 38.7% (95% CI: 10.0%, 60.8%).

• Reduced the risk of symptoms following infection.

  • RR = 41.2%; (95% CI: 18.9%, 67.7%).

Lower rate of seroconversion following infection.

• RR of seroconversion = 31.9% (95% CI: 22.3%, 44.6%).
• Consistent with less intense immune response to lower viral loads.

Shortened duration of detectable viral shedding:

• mAb: 6.2 days (95% CI: 5.0 days, 7.8 days),
• Placebo: 13.0 days (95% CI: 11.5 days, 14.6 days).

Wrapping up

Contributions on methodology and implementation

• Fit semi-Markov models to data with complex coarsening patterns.
• Algorithm is agnostic of model structure, but not a panacea for non-identifiability where it can fail loudly (a strength).
• Can accomodate splines + other flexible functions.
• Flexible and general impementation in R/Julia.

Methodological extensions (“low-hanging fruit”)

• Disease driven observation schemes + preferential sampling.
• Penalized splines and approximate cross-validation approximation for semi-parametric inference with automatic smoothing.
• Fast robust uncertainty quantification.
• Phase-type proposal distributions.

Please reach out if you want to collaborate on an analysis or methods! 😊 (jonathan.fintzi@bms.com)